Exploiting genomic approaches to identify the environmental basis of depression

Despite major advances in our understanding of depression’s genetic architecture, there remain major gaps in our understanding of its environmental risk factors that impede prevention.

Using studies of both related and unrelated individuals, I will identify the genetic changes and behaviours in those individuals that lead to depression in those individuals, as well as in their relatives.

I will compare these changes with the genetic signatures of lifestyle factors (e.g. obesity) and behaviours, such as smoking and alcohol consumption, to identify if they are environmental risk factors for depression.

In a complimentary approach, I will measure epigenetic changes in DNA methylation associated with depression and its risk factors.

I will use optimise these epigenetic measures to improve the prediction of depression and the measurement of its risk factors.

Finally, I will optimise the prediction of depression and its risk factors by applying these findings to prospective longitudinal dataset where depression and environmental risk factors have been measured on multiple occasions.

These studies will be used to refine the accuracy of each predictor and estimate how it would behave in unseen samples of individuals, in whom it may be applied for prevention in future studies.